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arXiv 2407.12010hep-lat
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用深度神经网络研究赝标胶球的质量

Study of the mass of pseudoscalar glueball with a deep neural network

American Association for the Advancement of Science(美国科学促进会)

Lin Gao

AI总结:

本研究基于格点QCD的蒙特卡洛模拟,用深度神经网络(DNN)从拓扑荷密度关联函数负部提取赝标胶球质量,与传统最小二乘拟合结果对比后,证实DNN是稳定互补的质量提取方法。

摘要:

基于蒙特卡洛模拟,采用深度神经网络(DNN)在格点量子色动力学(lattice QCD)框架下研究赝标胶球的质量。该DNN被构建用于从拓扑荷密度关联函数的负部提取质量,将所得质量估计值与传统最小二乘拟合的结果进行对比:DNN给出的赝标胶球质量为2558(89)MeV,传统拟合给出的结果为2612(112)MeV。结果表明,DNN为从格点关联函数提取质量提供了一种稳定且互补于传统方法的途径。

英文摘要:

A deep neural network (DNN) is utilized to study the mass of the pseudoscalar glueball in lattice QCD based on Monte Carlo simulations. The DNN is constructed to extract the mass from the negative part of the topological charge density correlation function. The resulting mass estimates are compared with those obtained from conventional least-squares fitting. The DNN gives a pseudoscalar glueball mass of 2558(89)MeV, while the conventional fit gives 2612(112)MeV. The results suggest that the DNN provides a stable and complementary approach to conventional mass extraction from lattice correlation functions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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